6. Electrical - IJEEE - Fuzzy Logic Based Maximum Power Tracking G.venkateswarlu

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FUZZY LOGIC BASED MAXIMUM POWER POINT TRACKING TECHNIQUE FOR

PARTIALLY SHADED PHOTOVOLTAIC SYSTEM

G. VENKATESWARLU1, C. H. JHANSI

2& P. S. RAJU

3

1Professor, Department of EEE, Narayana Engineering College, Gudur, Andhra Pradesh, India

2M.Tech Student, Department of EEE, Narayana Engineering College, S.P.S.R, Nellore, Andhra Pradesh, India

3Professor, HOD & Department of EEE, S. V. University, Tirupathi, Andhra Pradesh, India

ABSTRACT

Currentvoltage and powervoltage characteristics of large photovoltaic (PV) arrays under partially shaded

conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point(MPP) a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array

under these conditions. This paper proposes a modified fuzzy-logic controller for maximum power point tracking is

proposed to increase photovoltaic system performance during partially shades conditions. Instead of perturbing and

observing the PV system MPP, the controller scans and stores the maximum power during the perturbing and observing

procedures. The controller offers accurate convergence to the global maximum operating point under different partial

shadowing conditions. A mathematical model of the PV system under partial shadowing conditions is derived. To validate

the proposed modified fuzzy-logic-based controller, simulation results are provided.

KEYWORDS:Boost Converter, Fuzzy, Logic Controller (FLC), Maximum Power Point (MPP) Tracker (MPPT), Partial

Shadowing and Photovoltaic (PV)

INTRODUCTION

THE ever-increasing demand for low-cost energy and growing concern about environmental issues has generated

enormous interest in the utilization of nonconventional energy sources such as the solar energy. The freely and abundantly

available solar energy can be easily converted into electrical energy using photovoltaic (PV) cells. A PV source has the

advantage of low maintenance cost, absence of moving/rotating parts, and pollution-free energy conversion process.

However, PV systems suffer from a major drawback which is the nonlinearity between the output voltage and

current particularly under partially shaded conditions [5]. During partially shaded conditions, the systemPV characteristic

curve has multiple peaks. Therefore, a conventional maximum power point (MPP) tracker (MPPT) such as hill climbing,

incremental conductance, and ripple correlation could miss the global maximum point [6][9]. In general, a PV source is

operated in conjunction with a dcdc power converter, whose duty cycle is modulated in order to track the instantaneous

MPP of the PV source. Several tracking schemes have been proposed [2][12]. Among the popular tracking schemes are

the perturb and observe (P&O) or hill climbing [4], [5], incremental conductance [8], short-circuit current [2], open-circuit

voltage [7], and ripple correlation approaches [6]. Some modified techniques have also been proposed, with the objective

of minimizing the hardware or improving the performance [7], [8][9].

A study of partial shadowing conditions in [2] shows that using a conventional MPPT during partial shadowingcould result in significant losses of PV output power. Therefore, different researchers have investigated the limitation to

improve tracking efficiency. Noguchi et al. [9] propose a short-circuit pulse-based MPPT with fast scan on thePV curve

to identify the proportional parameter which is commonly used in a current-based MPPT. The global maximum point is

International Journal of Electrical and

Electronics Engineering (IJEEE)

ISSN(P): 2278-9944; ISSN(E): 2278-9952

Vol. 3, Issue 1, Jan 2014, 49-60

IASET


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50 G. Venkateswarlu, C. H. Jhansi & P. S. Rajufound; however, an additional switch in parallel with the PV source is required to compute the short-circuit current every

few minutes. Therefore, such a method causes momentary power losses and requires additional cost.

To avoid using an extra switch, the authors in [ 3] propose a controller that swings the converters duty cycle from

zero to one to measure the open-circuit voltage and the short-circuit current and then computes the optimum voltage and

current. From the computed values, the operating point is moved in one step to the optimal operating point.

Based on observation and investigation of thePV characteristic, the authors in [4] claim that, on either side of the

global maximum point, the local maxima consistently decrease. Therefore, an MPPT scheme for a PV system under partial

shadowing conditions is proposed based on this observation. In [5], a simulation of a partially shaded PV system rejects the

observation in [6], and an MPPT based on conventional hill climbing and a partial shadowing identifier has been proposed.

Also, it is observed that the global maximum point of a PV system under non uniform conditions is always located to the

left of maximum power at normal weather conditions. Therefore, a trajectory line of the PV system under different

isolation levels is stored in a data-based memory to identify the partially shaded conditions. Such a technique could savethe controller from having unnecessary global scan; however, the trajectory line is different between PV systems, and also,

the PV parameters vary with time. A line search algorithm with Fibonacci sequence has been employed to track the global

power point under partial shadowing conditions. However, this method can miss the global MPP in some partial shadowing

conditions [6]. In a direct search algorithm is employed to search the Lipchitz function which describes the PV power and

voltage relationship in an Interval In order for this method to ensure finding the global maximum power, the initial point

must be carefully selected; otherwise, the controller may be trapped at a local MPP. Practical swarm optimization has been

implied to track the maximum global operating point under abnormal weather conditions. A large time delay is required to

allow agents to compute the global maximum point, resulting in a long computation time to reach the maximum operating

point.

SYSTEM STRUCTURE

A PV system consists of a solar array which is a group of series-/parallel-connected modules, where the basic

building block within the module is a solar cell. Commercially, the solar cell rated power varies between 1 and 2 W

depending on the solar cell material and the surface area; therefore, to design a solar module, the solar cells power is

measured, and then, the modules are connected in series based on the desired output. Figure 1 shows a PV system.

Generally, the electrical characteristics of the PV system are represented by power versus voltage

(current/duty cycle) and by current versus voltage. The characteristic curves of the solar cell are nonlinear because ofoperational physical phenomena. By using the equivalent circuit of the solar cell shown in Figure 2, the mathematical

model of the generated current in a PV system is represented

Figure 1: PV Array Terminologies (a) PV Module (b) Series-Assembly with Two Series-Connected

Subassemblies S1 and S2 (c) Group (d) PV Array with Groups G1 to G4


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Fuzzy Logic Based Maximum Power Point Tracking Technique GOR Partially Shaded Photovoltaic System 51

Figure 2: Equivalent Circuit of a PV Cell

(1)Where Vpv and Ipv represent the PV array output voltage and current, respectively.Rs andRsh are the solar cell

series and shunt resistances, respectively. q is the electron charge (1.6 1019 C),Iph is the light generated current,Io is

the reverse saturation current,A is a dimensionless junction material factor, k is Boltzmanns constant (1.38 1023 J/K),

T is the temperature (in kelvins), and np and ns are the numbers of cells connected in parallel and series, respectively.

The characteristic curves of the PV array system depend on the radiation and temperature of the PV system. For a given

system, during normal conditions where the radiation is equally distributed among the PV modules, the powerduty cycle

(PD) characteristics under varying weather conditions are shown in Figure 3. However, when the radiation is not equally

distributed, local and global maxima are introduced in the characteristic curves. In order to understand such phenomena, a

PV array system with nine modules connected in series and parallel is considered, as shown in Figure 4. There are different

possibilities for the radiation distribution among the PV modules; the following five cases are randomly considered.

Case 1:One module in each column is completely shaded (viz., modules 1, 4, and 7).

Case 2:One module in each column is partially shaded with equal radiation levels (viz., modules 2, 5, and 8).

Case 3:One module in each column is partially shaded with unequal radiation levels (viz., modules 3, 6, and 9).

Figure 3: Influences of (a) Temperature (T) and (b) Solar Radiation (G) on the PD Characteristics


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52 G. Venkateswarlu, C. H. Jhansi & P. S. RajuCase 4:Two modules in the first column and one module in each other column are partially shaded with equal

radiation levels (viz., modules 1, 2, 4, and

Case 5:All modules are partially shaded with different radiation levels.

Simulation results for the five cases indicate that a completely shaded module causes a reduction of the PV output

power without creating local maxima. However, partially shaded modules result in a reduction of the PV output power,

creating local maxima, where the number of local maxima increases as the variation of the radiation levels on each module

increases. The PV output power characteristics for the five different cases are shown in Figure 5.

Figure 4: PV Array System with Nine Modules Connected in Series and Parallel

PV MODELING UNDER PARTIAL SHADING

During shadowing conditions, the PV system mathematical model in (1) is no longer valid because different

radiation around the PV system. Therefore, a new mathematical model is required to represent the PV system under partial

shadowing conditions. An extensive study has been undertaken for different PV module connections to derive a general PV

mathematical model under shadowing conditions. For simplicity, three series-connected PV modules are considered.

Figure 5: PV Output Power Characteristics under the Five Cases

Based on the data for the Shell SP150-PC, the short-circuit current and the open-circuit voltage for each PV

module under rated radiation level are 4.4 A and 43.4 V, respectively. One PV module is partially shaded, and it receives a

radiation of 500 W/m2, while the other two modules receive the rated radiation, which is 1000 W/m2. The three points are

considered: point 1 where the current equals Isc and the voltage equals zero, point 2 where the current equals Istep and the

voltage equals Vstep, and point 3 where the current equals zero and the voltage equals Voc. By inspection, the previous

variables can be defined as follows.


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Fuzzy Logic Based Maximum Power Point Tracking Technique GOR Partially Shaded Photovoltaic System 53 Isc is the short-circuit current of the un-shaded PV modules. Istep is the short-circuit current of the shaded PV module. Vstep is the summation of the open-circuit voltages of the un-shaded modules. Voc is the summation of the open-circuit voltages of the shaded and un-shaded modules.

From these observations, the mathematical model of the given PV system is (2), shown at the bottom of the next

page, where nus s and nss are the numbers of series un-shaded and shaded modules, respectively, and us and s are the

radiation levels on the un-shaded and shaded modules, respectively. Equation (2) is valid for two radiation levels

distributed around the series-connected PV modules. Therefore, (2) is extended to handle three radiation levels, as in (3).

When the radiation distribution levels increase, the number of voltage/ current steps increases. Figure 7 shows the IV

characteristic of three series-connected PV modules with different shadowing levels. The PV system is tested under

different partial shadowing conditions. Two PV modules are partially shaded and receive two different radiation levels,

which are 500 and 300 W/m2, and the third module receives rated radiation, which is 1000 W/m2. Two more observations

are added to the previous observations as follows.

Istep 2 is the short-circuit current of the shaded PV modules with the highest radiation level. Vstep 1 is the open-circuit voltage of the unshaved modules plus the summation of the shaded module open circuit

voltages without the open-circuit voltage of the lowest radiation module.

Therefore, the mathematical model for three different radiation levels on three series-connected PV modules

is (3), shown at the bottom of the page, where ns1s is the number of partially shaded PV modules with the lowest radiation

level and ns2s is the number of partially shaded PV modules with the highest radiation level.[4] s1 is the lowest radiation

level, and s2 is the highest radiation level. The general mathematical model of n series connected PV modules in a PV

system is (4), shown at the bottom of the next page, where nsN s is the number of partially shaded PV modules with the

highest radiation level and sN is the highest radiation level. N is the number of distributed radiation levels. Usually, the

PV system consists of parallel-/series-connected PV modules [2]. Therefore, to drive a mathematical model for the overall

PV system, the previous three series-connected PV modules are connected in parallel with another three series connected

PV modules, as for simplicity, the radiation levels are distributed as follows. In the first branch, modules 1 and 2 receive a

radiation of 1000 W/m2, and the shaded module receives 500 W/m2

(2)From (2), the general mathematical model ofNparallel connected PV modules in the PV system is

(3)PROPOSED METHOD

If the difference between the identified maximum power and the operated power is greater than the preset value,

the duty cycle is increased; otherwise, fuzzy-logic-based MPPT is applied. In this the algorithm ensures that the MPPT is

not trapped by local maxima and quickly recovers the new global maximum point during varying weather conditions.

Unlike conventional scanning MPPT,[11] the essentiality of using a long time delay is not required because the controller

scans thePV curve while perturbation and observation are carried out[7]. Figure 6 shows the flowchart of the proposed

method, where Vpv andIpv are the PV output voltage and current, respectively,D is the duty cycle,Pm is the global MPP,


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54 G. Venkateswarlu, C. H. Jhansi & P. S. Rajuand Pm is a constant that identifies the allowable difference between the global maximum point and the operating power

point.

The second technique is to increase the duty cycle from a minimum to a maximum value with a fixed step. In this

case, thePV curve is scanned, and the global MPP is stored. The last technique is to apply a large initial perturbation step

to make a wide search range on the PV power locus.

Figure 6: Proposed Method Flowchart

Figure 7: PV Array System Blocks Diagram and the Proposed MPPT Controller

FLC DESIGN

Modification to the fuzzy-logic-based MPPT algorithm, using the scanning and storing procedures, is proposed to

quickly locate the global MPP. Fuzzification of the flowchart in Figure 7 is considered in the proposed MPPT design [7].

The inputs to the fuzzy-logic controller (FLC) are

(4) (5)

(6)And the output equation is

(7)


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Fuzzy Logic Based Maximum Power Point Tracking Technique GOR Partially Shaded Photovoltaic System 55Where P and I are the PV array output power change and current change, respectively, PM is the difference

between the stored global maximum power (PM) and the current power, and D is the boost converter duty cycle

change [12]. To ensure that the PV global maximum power is stored during the scanning procedure, a fast initial tracking

speed is used. The variable inputs P and I are divided into four fuzzy subsets: positive big (PB), positive small (PS),negative big (NB), and negative small (NS). The variable input PM is divided into two fuzzy subsets: PB and PS.

The output variable D is divided into six fuzzy subsets: PB, positive medium (PM), PS, NB, negative medium (NM), and

NS. Therefore, the fuzzy algorithm requires 32 fuzzy control rules;

Table 1: Fuzzy-Logic Rules

The shapes and fuzzy subset attritions of the membership function in both of the inputs and output are shown in

Figure 8.

Figure 8: Membership Functions: (a) Input P, (b) Input I, (c) Input PM, and (d) Output D

The last fuzzy controller stage is defuzzification where the center-of-the-area algorithm is used to convert the

fuzzy subset duty cycle changes to real numbers

(8)Where D is the fuzzy controller output and Di is the center of the maxmin composition at the output

membership function.

The surface view of the fuzzy logic controller is as show in the figure 9 as show in the figure below


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56 G. Venkateswarlu, C. H. Jhansi & P. S. Raju

Figure 9: Surface View of the FLC

SIMULATION RESULTS

The simulation circuit as shown in the figure below with temperature and the irradiance is the andsimulation results is all so show in the figure

Figure 10: Simulation Circuit Diagram

Figure 11: Output Voltage with Fuzzy Logic Controller


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Figure 12: Output Current with Fuzzy Logic Controller

Figure 13: Output Power with Fuzzy Logic Controller

CONCLUSIONS

In this paper, a fuzzy-logic-based MPPT has been proposed to extract the global MPP under partially shaded PV

system conditions. The proposed MPPT has been implemented by combining fuzzy-logic-based MPPT with a scanning

and storing system. Three scanning techniques have been proposed to scan the PV power characteristic curve and store the

Maximum power value, during initial and varying weather conditions. A new mathematical model has been

proposed to represent the behavior of the PV characteristic under partial shadowing conditions. Mat lab/Simulink

simulations and practical Experiments of a partially shaded PV system have Been carried out to validate the proposed

MPPT. The results show that the proposed MPPT is able to reach the global MPP under any partial shading conditions.

Moreover, the controller exhibits a fast converging speed, with small oscillation around the MPP during steady state.

REFERENCES

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58 G. Venkateswarlu, C. H. Jhansi & P. S. Raju5. G. Lijun, R. A. Dougal, L. Shengyi, and A. P. Iotova, Parallel -connected solar PV system to address partial and

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AUTHORSDETAILS

G. Venkateswarlu has received his B.Tech degree from J.N.T.University, Ananthapur. M.Tech degree with the

specialization of Electrical PowerEngineeringfrom J.N.T.University. At present he is working as an Associate Professor in

Narayana Engineering College, Nellore. He had 5 years of experience in teaching field. He had published many papers inpower system applications. Present he is interested in photo voltaic cell, facts controller, power system planning.

C. H. JhansiReceived the B.Tech Degree From: P.B.R.Visvodaya Institute of Technology and science, 2011

passed out, Kavali. Affiliated to JNTU Ananthapur. Present She Scholar M.Tech From Narayana Engg. College, Nellore,

Affiliated to JNTU Ananthapur Current Research Interested Areas Power System and Solar Energies.


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Dr. P. Sangameswara Raju, He is presently working as a Professor in SVU college of Engineering Tirupathi.

Obtained his diploma and B.Tech in Electrical Engineering, M.Tech in power system operation and control and PhD in

S.V. University, tirupati. His areas of interest are power system operation, planning and application of fuzzy logic to power

system, application of power system like on linear controllers, on conventional Energy sources.


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6. Electrical - IJEEE - Fuzzy Logic Based Maximum Power Tracking G.venkateswarlu